Automatic Adaptation of Knowledge Structures for Assisted Information Seeking (AutoAdapt)

大量的电子文档集合存在于公司、大学和其他机构中。两种常见的信息寻求形式是搜索和探索（浏览）的集合。然而，在这些集合中找到相关信息可能是困难的。这对于使用不太明确和不太具体的查询进行搜索以及对于用户可能没有特定搜索目标的浏览来说都是如此。用户的信息搜索可以得到结构良好的知识搜索领域，我们称之为域模型。领域模型实际上是一种结构，人们将其强加于数据，以支持他们进行信息搜索。我们现在可以直接从域模型中获得查询修改或浏览建议。为了用一个实际的例子来说明这一点，假设埃塞克斯大学内部网的一个用户从搜索联合开始。这个查询将触发搜索系统提供查询细化条款，如学生会和欧盟。事实上，所有的本地网站、内部网和类似的集合都包含大量有价值的领域知识，这些知识是隐式编码的。面临的挑战是自动获取域模型，然后通过帮助用户进行搜索或浏览等信息搜索任务来使其可用。一个更大的挑战是自动演化这个域模型。该建议的新奇在于通过观察用户对领域知识的使用情况并相应地对其进行修改来自动获取领域知识。我们假设提交的用户查询和用户与搜索系统之间的对话可以被监控，并用于随着时间的推移改进域模型。用户对查询修改建议的选择被视为相关性的指示。然后，这可以用来更新领域知识，从而帮助下一个用户提供类似的查询，提出更新的查询修改suggestions.This项目的目的是开发和评估方法，自动构建域模型，以适应人口的用户的搜索或浏览行为。应用程序和大规模的评估开发的方法在两个信息寻求的情况下-即，交互式搜索和浏览-将在一些域，包括埃塞克斯大学，开放大学和我们的工业合作伙伴的内联网上进行。

A massive number of electronic document collections exist within companies, universities and other institutions. Two common forms of information seeking are searching and exploring (browsing) the collections. However, finding relevant information within such collections can be difficult. This is true for searching with poorly formulated and less specific queries as well as for browsing where the user may not have a specific target to search. The user's information seeking could be assisted by well-structured knowledge about the search domain, which we refer to as domain model. A domain model is effectively a structure that people impose on data to support them in information seeking. We can now derive query modification or browsing suggestions directly from the domain model. To illustrate the point using a realistic example, assume a user of the University of Essex intranet started by searching for union . This query would trigger the search system to offer query refinement terms such as students union and european union . Indeed, all local Web sites, intranets and similar collections do contain a huge amount of valuable domain knowledge that is encoded implicitly. The challenge is to automatically acquire a domain model and then make it usable by assisting users in information seeking tasks such as searching or browsing. An even bigger challenge is to evolve this domain model automatically. The novelty of this proposal lies in evolving automatically acquired domain knowledge by observing users' usage of it and altering it accordingly. We hypothesize that the submitted user queries and the dialogues between users and search system can be monitored and used to improve the domain model over time. A user's selection of a query modification suggestion is taken as an indication of relevance. This can then be used to update the domain knowledge and thus help the next user with a similar query by presenting updated query modification suggestions.This project aims to develop and evaluate methods for adapting automatically constructed domain models to the population of users' search or browsing behaviour. Application and large-scale evaluation of the developed methods in two information seeking scenarios - namely, interactive search and browsing - will be performed on a number of domains including the intranets of the Essex University, the Open University and our industrial partners.